PolicyPal allows customers to buy and manage their insurance policies via mobile. Watson was trained on PolicyPal's database of more than 9,000 insurance policies to be able to gauge the intent of customer queries intuitively, and answer their questions quickly.

It has also been trained to explain complex insurance jargon to consumers to improve their understanding of the various insurance products available to them. Watson Conversation uses natural language processing (NPL) and machine learning (ML) to simulate natural human conversation to put consumers at ease. PolicyPal says the chatbot will help its users make more informed, data-driven decisions when it comes to choosing, buying, and upgrading their coverage. Larger insurers have been jumping in with regard to AI by putting algorithms in charge of claims handling — but given ongoing imperfections in the technology, beginning with a more limited experiment like a chatbot could help ease both insurers and consumers into using AI and limit any growing pains.

Difficult market conditions and competition from digital savvy entrants have been challenging traditional banks. But a solution is emerging that will enable these legacy players to innovate at relatively modest costs: chatbots.

Chatbots are software programs that primarily use business-to-consumer (B2C) text-based messaging as the interface through which to carry out any number of tasks. They appeal to banks because they require less coding and are therefore cheaper than banking apps; they also automate currently manual back-end tasks, saving them money on salaries and allowing some operations to run 24/7.

Maria Terekhova, research analyst for BI Intelligence, Business Insider's premium research service, has compiled a detailed report on chatbots in banking that looks at the drivers behind the proliferation of chatbots among incumbent banks, current chatbot use cases and their growing variety, and the strategic, consumer, and technological risks still attached to chatbots.

Here are some of the key takeaways:

Incumbent banks today are facing increasing pressure to remain competitive. The pressure is coming mostly from tech-savvy entrants that lure in consumers with user-friendly, cheaper products.

To remain competitive, these legacy players must innovate digitally, and chatbots let them do so on a budget. Either a third-party provider can build a chatbot for them to roll out on a popular messaging app, or they can develop a chatbot in-house.

Chatbots still have risks attached to them, but they are outweighed by their benefits. If technological, strategic, and consumer risks aren't properly navigated, a chatbot can damage a bank's valuable reputation. However, a well-executed chatbot can deliver huge savings.

A successful chatbot has to be able to perform a task more efficiently than can be done manually. As such, banks should develop chatbots that effectively automate basic and time-consuming tasks. This will free up human staffers' time for more complex inquiries, thus improving customer relations and loyalty.

In full, the report:

Explains the factors driving chatbot proliferation generally and among legacy banks in particular.

Looks at the developments in the technology underlying chatbots.

Provides an overview of current legacy players' chatbot use cases.

Examines the risks still attached to chatbots and how to navigate them.

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